
Rule-based recommendation systems like  [1]}, [2]} have obvious limitations when it comes to adapting to new data tables and new types of visualizations. Also machine learning based models such as  [3]}, [4]} require complete re-training of the complete dataset in such a setup. VizAI is highly flexible and only requires additional training on new data to adapt to the new setup.

None of these works provide quantification on how well the recommended plots are able to depict the data they were created on. Based on our previous studies, we know the importance of such a quantification, especially for visualisations of data in the numeric domain. Overcoming this limitation, VizAI tries to generate a score for each visualization type based on it's ability to depict data statistics easily.

